Charles Sturt University - STA201: Scientific Statistics (Solutions)

Statistics is the science and art of making decisions in the presence of variability. This subject provides a foundation in the basic practice of statistics. The subject's orientation is towards the sciences and covers both experimental and observational data. The emphasis is on understanding statistical concepts and applying acquired skills to data interpretation by the use of a modern software package.

Major topics to be covered:

• Descriptive statistics: Graphs and statistics as summaries, frequency tables, concept of population and sample, random variation, measurement levels (categorical, ordinal, interval, ratio), measures of location and dispersion, mean and median, standard deviation, range and coefficient of variation
• Basic probability: Probability estimate from relative frequency and percentiles, conditional probability
• Random variables: Representing the outcome of experiments in terms of random variables, distinguishing between discrete and continuous random variables, calculating probabilities using the Binomial, Poisson and Normal distributions
• Sampling distributions: The application of various sampling strategies and the distribution of sample means and proportions
• Inference based on a single sample: Using a sample to gain information about a population, distribution of sample means and proportions, confidence intervals, hypothesis tests
• Inference based on two independent samples: Using samples to gain information about populations, controlled experimentation, distribution of differences in sample means or proportions, confidence intervals, hypothesis tests
• Analysis of variance: One-way Analysis of Variance for testing differences in more than two means, graphical displays, checks on assumptions and post hoc tests
• Chi-square tests: Testing whether data fit a specified distribution, checking distributions graphically, and testing for independence of two categorical variables
• Simple linear regression and correlation: Estimating the intercept and slope of the Least Squares line, testing the slope of the regression line, interpreting coefficient of determination (R squared) and correlation; and,
• Experimental design: Difference between experimental and observational data, randomisation, causality, replication, blocking, sample size